






def      _snake_case     (__lowercase):
   UpperCamelCase_						=  [1]

   UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_						=  0, 0, 0
   UpperCamelCase_						=  ugly_nums[ia] * 2
   UpperCamelCase_						=  ugly_nums[ia] * 3
   UpperCamelCase_						=  ugly_nums[ia] * 5

   for _ in range(1     ,   __lowercase):
      UpperCamelCase_						=  min(__lowercase     ,   __lowercase     ,   __lowercase)
      ugly_nums.append(__lowercase)
      if next_num == next_a:
         ia += 1
         UpperCamelCase_						=  ugly_nums[ia] * 2
      if next_num == next_a:
         ia += 1
         UpperCamelCase_						=  ugly_nums[ia] * 3
      if next_num == next_a:
         ia += 1
         UpperCamelCase_						=  ugly_nums[ia] * 5
   return ugly_nums[-1]


if __name__ == "__main__":
 from doctest import testmod

 testmod(verbose=True)
 print(f'{ugly_numbers(2_0_0) = }')







import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class 							_UpperCamelCase					(       _A  ,							_A  ,							unittest.TestCase	):






			'''simple docstring'''

			__UpperCamelCase   :    str          =			IFInpaintingSuperResolutionPipeline
			__UpperCamelCase   :    str          =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
			__UpperCamelCase   :    Optional[Any]          =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}	)
			__UpperCamelCase   :    Dict          =			PipelineTesterMixin.required_optional_params - {"""latents"""}

			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							return self._get_superresolution_dummy_components()

			def    lowerCAmelCase__						(							self			:							Dict    ,						snake_case_			:							Optional[int]    ,						snake_case_			:							str=0   ):
							if str(snake_case_   ).startswith("""mps"""   ):
											UpperCamelCase_:				Union[str, Any]						=    torch.manual_seed(snake_case_   )
							else:
											UpperCamelCase_:				Tuple						=    torch.Generator(device=snake_case_   ).manual_seed(snake_case_   )

							UpperCamelCase_:				Dict						=    floats_tensor((1, 3, 16, 16)    ,						rng=random.Random(snake_case_   )   ).to(snake_case_   )
							UpperCamelCase_:				int						=    floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(snake_case_   )   ).to(snake_case_   )
							UpperCamelCase_:				str						=    floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(snake_case_   )   ).to(snake_case_   )

							UpperCamelCase_:				Dict						=    {
							    """prompt""": """A painting of a squirrel eating a burger""",
							    """image""": image,
							    """original_image""": original_image,
							    """mask_image""": mask_image,
							    """generator""": generator,
							    """num_inference_steps""": 2,
							    """output_type""": """numpy""",
							}

							return inputs

			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()    ,						reason="""XFormers attention is only available with CUDA and `xformers` installed"""    ,						)
			def    lowerCAmelCase__						(							self			:							Tuple   ):
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )

			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							self._test_save_load_optional_components()

			@unittest.skipIf(torch_device != """cuda"""    ,						reason="""float16 requires CUDA"""   )
			def    lowerCAmelCase__						(							self			:							List[Any]   ):
							# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
							super().test_save_load_floataa(expected_max_diff=1e-1   )

			def    lowerCAmelCase__						(							self			:							str   ):
							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )

			def    lowerCAmelCase__						(							self			:							str   ):
							self._test_save_load_local()




			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							self._test_inference_batch_single_identical(
							    expected_max_diff=1e-2    ,						)






